GTD
Sunil K S, Srivastav N, Shrinjit Das, Tushar Singh, Tanay Chaudhari
6 September 2018
gtd12to15= read.csv('gtd_12to15_52134.csv')
gtd70to91= read.csv('gtd_70to91_49566.csv')
gtd92to11= read.csv('gtd_92to11_no 93_55072.csv')
gtd1993= read.csv('gtd1993_748.csv')
a=merge(gtd12to15,gtd70to91,all=TRUE)
b=merge(gtd92to11,gtd1993,all=TRUE)
GTD=merge(a,b,all=TRUE)library(readxl)
library(data.table)
library(dplyr)
library(ggplot2)
library(reshape2)
library(plotly)
library(tidyr)
library(graphics)
library(ggridges)Q1) Number of Attacks per year ?
Q1=GTD%>%group_by(iyear)%>%summarise(`No Of Attacks`=sum(iyear=n()))
# Plot
theme_set(theme_grey())
aa=ggplot(Q1,aes(x=iyear,y=`No Of Attacks`))+
geom_area(fill='lightyellow3',col='lightyellow4',size=1)+
geom_point(col="goldenrod4",size=1)+
scale_x_continuous(breaks = seq(1970,2015,5))+
scale_y_continuous(breaks = seq(500,20000,1000))+
labs(subtitle='From 1970 to 2015',y='No Of Attacks', x='Years', title='NO OF ATTACKS PER YEAR')
ggplotly(aa)Q2) Number of bombing per year
Q2=GTD%>%filter(attacktype1_txt=='Bombing/Explosion')%>%group_by(iyear)%>%summarise(`no of bombing`=sum(attacktype1_txt=n()))
#plot
theme_set(theme_gray())
bb=ggplot(Q2,aes(x=iyear,y=`no of bombing`))+
geom_area(fill='skyblue4',col='skyblue',size=1)+
geom_point(col="grey27",size=1,y=Q2$`no of bombing`)+
labs(subtitle='Red=No Of Attacks, Blue=No Of Bombing' ,y='No Of Attacks and Bombings', x='Years', title='NO OF BOMBING PER YEAR')+
scale_x_continuous(breaks = seq(1970,2015,5))+
scale_y_continuous(breaks = seq(100,20000,1000))
ggplotly(bb)Q3) Terrorist attacks region wise per year
Q3=GTD%>%filter(doubtterr==0)%>% group_by(iyear,region_txt)%>%summarise(`No Of Attack`=n())
#plot
we=ggplot(Q3, aes(x=region_txt, y=iyear)) +
geom_point(aes(col=region_txt, size=`No Of Attack`)) +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)+
theme(legend.position="bottom")+
labs(title="Terrorist attacks region wise per year",
y="Year",
x="Region") +
theme(axis.text.x = element_blank())
ggplotly(we)Q4) Top 5 type of terror attacks per region
Q4=GTD%>%filter(doubtterr==0)%>%group_by(region_txt,attacktype1_txt)%>%
summarise(no_of_attacks=sum(attacktype1_txt=n()))%>%
arrange(region_txt,-no_of_attacks)%>%top_n(5)## Selecting by no_of_attacksggplot(Q4,aes(x=attacktype1_txt,y=no_of_attacks))+
geom_bar(stat = "Identity",width = .5, aes(fill =attacktype1_txt))+
theme(legend.title = element_blank())+
theme(legend.position = "bottom", legend.key.width = unit(.2, "cm"))+
labs(subtitle='From 1970 to 2015',y='No Of Attacks', x='Region', title='Top 5 type of terror attacks per region')+
theme_bw() + facet_wrap(~region_txt, scales="free",ncol=3)+
theme(legend.title = element_blank())+
theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank())
- Heaviest hit Target types (Based on both Killed and wounded)
Q5=GTD%>% group_by(targtype1_txt)%>%
summarise(`Heaviest hit Target types`=sum(round(sum(nkill, na.rm = TRUE)),round(sum(nwound, na.rm=TRUE))))%>%
arrange(-`Heaviest hit Target types`)%>%head(10)
ff=ggplot(Q5,aes(x=reorder(targtype1_txt,-`Heaviest hit Target types`),y=`Heaviest hit Target types`))+
geom_bar(aes(fill=targtype1_txt),stat = "Identity",width=.9)+
theme(legend.position = "bottom", legend.key.width = unit(2, "cm"))+
theme_bw()+
theme(legend.title = element_blank())+
scale_y_continuous(breaks = seq(10000,300000,50000))+
theme(axis.text.x=element_blank(),axis.ticks.x = element_blank())+
labs(subtitle='From 1970 to 2015',y='Total Casualities', x='Target Type', title='Heaviest hit Target types (Based on both Killed and wounded)')
ggplotly(ff)- Terrorist attack in India and Pakistan in last 45 years
Q6=GTD%>%group_by(country_txt, iyear)%>%filter(country_txt=='Pakistan'|country_txt=='India', doubtterr==0) %>%
filter(iyear>(max(GTD$iyear, na.rm = T)-45))%>%summarise(Year_wise=n()) %>%arrange(-Year_wise) %>%
ggplot(aes(x=iyear, y=Year_wise, fill=country_txt))+
geom_area(position = 'stack', alpha=.99)+geom_line()+theme_dark()+
scale_x_continuous(breaks = seq(1971,2015,5))+
scale_fill_manual(values = c("#E7B800","#00AFBB"))+
geom_point(aes(x=iyear))+
facet_grid(~country_txt)+
labs(title="IND Vs PAK TERROR-ATTACKS",subtitle="1970-2015", x="YEAR WISE",y="ATTACKS",fill="COUNTRIES")
ggplotly(Q6)Q7. Terror attack in United States vs Russian Federation/USSR in last 45 years?
Q7=GTD%>%filter(doubtterr==0 & (country_txt=="United States" | country_txt=="Russia"|country_txt=="Soviet Union"))%>%group_by(iyear,country_txt)%>%summarise(`No Of Attacks`=sum(iyear=n()))%>%arrange(`No Of Attacks`)
Q7$country_txt <- as.character(Q7$country_txt)
Q7$country_txt[Q7$country_txt=="Soviet Union"] = "Russia/Soviet Union"
Q7$country_txt[Q7$country_txt=="Russia"] = "Russia/Soviet Union"
Q7$country_txt <- as.character(Q7$country_txt)
Q7_1=Q7%>%group_by(iyear,country_txt)%>%summarise(total=sum(`No Of Attacks`))
View(Q7_1)
hh=ggplot(Q7_1,aes(x=iyear,y=total))+
geom_bar(stat = 'identity',aes(fill=country_txt,width = .5))+
theme_bw() +
labs(subtitle='From 1970 to 2015',y='No Of Attacks', x='Country', title='Terror attack in United States vs Russian Federation/USSR in last 45 years')## Warning: Ignoring unknown aesthetics: widthggplotly(hh)Q8. Where are there the most casualties ?
Q8=GTD%>%group_by(country_txt)%>%summarise(`No Of Casuality`=round(sum(sum(nkill,na.rm=T),sum(nwound,na.rm=T))))%>%arrange(-`No Of Casuality`)%>%head(10)
View(Q8)
ii=ggplot(Q8,aes(x=reorder(country_txt,-`No Of Casuality`),y=`No Of Casuality`))+
geom_bar(stat="Identity",aes(fill =country_txt),width = .40)+
geom_text(aes(label = `No Of Casuality`),vjust = -0.25)+
theme_bw()+
theme(axis.text.x = element_blank())+
theme(legend.position = "bottom",legend.title = element_blank())+
theme(legend.text = element_text(colour = 'black', size = 8,face = 'bold'))+
labs(subtitle='From 1970 to 2015',y='No Of Casualities', x='Year', title='Where are there the most casualties')
ggplotly(ii)Q9.How have casualties evolved throughout the years?
Q9=GTD%>%group_by(iyear)%>%summarise(`Total Kills`=round(sum(nkill,na.rm=T)),`Total Wounded`=round(sum(nwound,na.rm=T)),`Total Casualities`=`Total Kills`+`Total Wounded`)
View(Q9)
jj=ggplot(Q9,aes(x=iyear))+
#geom_line(stat="Identity")+
geom_line(aes(y=Q9$`Total Kills`),col='red',size=1)+
geom_line(aes(y=Q9$`Total Wounded`),col='blue',size=1)+
labs(subtitle='Kills=Red Wounded=Blue',y='No Of Casualities', x='Year', title='How have casualties evolved throughout the years')+
theme_bw()+
theme(axis.text.x = element_text())+
theme(legend.position = " ")+
scale_x_continuous(breaks = seq(1970,2015,5))+
scale_y_continuous(breaks = seq(80,44000,5000))+
geom_point(aes(y=Q9$`Total Kills`),col='Black',size=1)+
geom_point(aes(y=Q9$`Total Wounded`),col='black',size=1)+
theme(legend.text = element_text(colour = 'black', size = 8,face = 'bold'))
ggplotly(jj)Q10. What are the casualties by weapon type?
Q10=GTD%>%group_by(weaptype1_txt)%>%
summarise(`No Of Casuality`=round(sum(sum(nkill,na.rm=T),sum(nwound,na.rm=T))))
View(Q10)
ggplot(Q10,aes(x=reorder(weaptype1_txt,-`No Of Casuality`),y=`No Of Casuality`))+
geom_bar(aes(fill=weaptype1_txt),stat="Identity",width=(.9))+
geom_text(aes(label=`No Of Casuality`),vjust = -0.25)+
theme(legend.position = "bottom", legend.key.width = unit(0.5, "cm"),legend.title = element_blank())+
scale_y_continuous(breaks = seq(0,500000,50000))+
theme(axis.text.x = element_blank())+
labs(subtitle='From 1970 to 2015',y='Total Casualities', x='Target Type', title='Casualties by weapon')
Q11. Are certain nationalities more targeted? If yes, which one ?
Q11=GTD%>%filter(natlty1_txt!='.')%>% group_by(natlty1_txt)%>%summarise(most.targated=n())%>%arrange(-most.targated)%>%head(10)
View(Q11)
ll=ggplot(Q11,aes(x=reorder(natlty1_txt,-most.targated),y=most.targated))+
geom_bar(aes(fill=natlty1_txt),stat="Identity",width=(.9))+
theme(legend.position = "bottom", legend.key.width = unit(0.7, "cm"))+
scale_y_continuous(breaks = seq(3000,18000,5000))+
theme(axis.text.x = element_text(angle = -45))+
geom_text(aes(label=most.targated),vjust = -0.25)+
theme(axis.text.x = element_blank(),legend.title = element_blank())+
labs(subtitle='From 1970 to 2015',y='Most Targeted', x='Nationality', title='nationalities more targeted')
ggplotly(ll)Q12. Are some countries better at defending themselves against terrorist attacks?If yes, which is the safest country to live
#success proportion
total_attack = GTD %>% group_by(country_txt) %>% summarise(totalattack = n())%>%arrange(-totalattack)%>% head(30)
number_defend = GTD %>%filter(success == 0) %>% group_by(country_txt) %>% summarise(count = n())
merge_table3 = merge(x = number_defend,y = total_attack)
defend_prop = merge_table3 %>% group_by(country_txt) %>% summarise(prop = round(count/totalattack,3))%>%
arrange(-prop)
View(defend_prop)
temp1tab=merge(total_attack,defend_prop)%>%arrange(-prop)%>%head(10)
View(temp1tab)####4
ggplotly(ggplot(temp1tab,aes(x=reorder(country_txt,-prop),y=prop))+
geom_bar(stat="identity",aes(fill=country_txt))+
labs(title='Sucesses rate in defending attacks')+
theme(axis.text.x = element_blank(),legend.title = element_blank()))## lower number of extended attacks
## proportion between total attacks and extended attacks
extended_attack = GTD %>% filter(extended == 1) %>% group_by(country_txt) %>% summarise(count = n()) %>%
arrange(-count)
total_attack = GTD %>% group_by(country_txt) %>% summarise(totalattack = n())%>%arrange(-totalattack)%>% head(30)
merge_table1 = merge(total_attack,extended_attack)
prop_extended_attack = merge_table1%>% group_by(country_txt) %>%
summarise(prop = round(count/totalattack,3)) %>% arrange(-prop) %>% tail(10)
mergetab3 = merge(prop_extended_attack,total_attack) %>% arrange(-totalattack)####
View(mergetab3)
ggplotly(ggplot(mergetab3,aes(x=reorder(country_txt,prop),y=prop))+geom_bar(stat="identity",aes(fill=country_txt))+
labs(title='Attacks Extended Less')+
theme(axis.text.x = element_blank(),legend.title = element_blank()))#proportion of kidnap and rescued:
number_kidnap = GTD %>% filter(ishostkid == 1) %>% filter(nhostkid != -99)%>% filter(nreleased != -99)%>%
group_by(country_txt) %>% summarise(total_kidnap = sum(nhostkid)) %>% arrange(-total_kidnap)
View(number_kidnap)
number_release = GTD %>% filter(ishostkid == 1) %>% filter(nhostkid != -99) %>% filter(nreleased != -99) %>%
group_by(country_txt)%>%summarise(total_release = sum(nreleased))
merge_table2 = merge(x = number_kidnap,y= number_release) %>% group_by(country_txt) %>%
summarise(prop.rescued = round(total_release/total_kidnap,3)) %>% arrange(-prop.rescued)%>%
head(10)
View(merge_table2)
mergetable2=merge(total_attack,merge_table2)####
View(mergetable2)
ggplotly(ggplot(mergetable2,aes(x=reorder(country_txt,-prop.rescued),y=prop.rescued))+
geom_bar(stat="identity",aes(fill=country_txt))+
labs(title='Kidnapped and Rescued prop')+
theme(axis.text.x = element_blank(),legend.title = element_blank()))